CN110367897A - Control method, system, equipment and the storage medium of the automatic warm dish of smart machine - Google Patents
Control method, system, equipment and the storage medium of the automatic warm dish of smart machine Download PDFInfo
- Publication number
- CN110367897A CN110367897A CN201910677687.5A CN201910677687A CN110367897A CN 110367897 A CN110367897 A CN 110367897A CN 201910677687 A CN201910677687 A CN 201910677687A CN 110367897 A CN110367897 A CN 110367897A
- Authority
- CN
- China
- Prior art keywords
- time
- time point
- prediction model
- tableware
- day
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L15/00—Washing or rinsing machines for crockery or tableware
- A47L15/0018—Controlling processes, i.e. processes to control the operation of the machine characterised by the purpose or target of the control
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L15/00—Washing or rinsing machines for crockery or tableware
- A47L15/42—Details
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61L—METHODS OR APPARATUS FOR STERILISING MATERIALS OR OBJECTS IN GENERAL; DISINFECTION, STERILISATION OR DEODORISATION OF AIR; CHEMICAL ASPECTS OF BANDAGES, DRESSINGS, ABSORBENT PADS OR SURGICAL ARTICLES; MATERIALS FOR BANDAGES, DRESSINGS, ABSORBENT PADS OR SURGICAL ARTICLES
- A61L2/00—Methods or apparatus for disinfecting or sterilising materials or objects other than foodstuffs or contact lenses; Accessories therefor
- A61L2/24—Apparatus using programmed or automatic operation
Landscapes
- Health & Medical Sciences (AREA)
- Epidemiology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention discloses control method, system, equipment and the storage medium of a kind of automatic warm dish of smart machine, the control method includes: the historical time point for obtaining user in history set period of time and taking out tableware from smart machine in different usage time intervals daily；By n-th day in history set period of time as input, using the corresponding historical time point of usage time interval different in n-th day as output, the prediction model for taking out the time point of tableware in different usage time intervals daily for obtaining user is established；Obtain the M days in target time section；It is input within M days prediction model by the, obtains the object time point of the M days different usage time intervals taking-up tablewares of the user in target time section；The warm dish function that duration opens smart machine is set in advance according to object time point.The present invention obtains the use habit of user by analysis, adaptively heats in advance to tableware, to improve the usage experience of user.
Description
Technical field
The present invention relates to smart machine automatic control technology field, in particular to the control of the automatic warm dish of a kind of smart machine
Method, system, equipment and storage medium processed.
Background technique
With the fast development of the intellectualized technology of smart machine, requirement of the user to intellectual product is consequently increased, such as
Intelligent dish washing machine can be realized wash dishes are clean and eliminate artificial participation；However, existing dish-washing machine can be only done pair
The cleaning function of tableware, but user every time from dish-washing machine take out tableware when tableware be all it is ice-cold, to influence user's
Usage experience；Particularly with winter, ice-cold tableware but will bring poor experience sense to user.
Summary of the invention
The technical problem to be solved by the present invention is in order to overcome in the prior art dish-washing machine Chinese dinner service take out when be ice-cold
, to bring the defect of poor experience sense to user, a kind of control method of the automatic warm dish of smart machine is provided, is
System, equipment and storage medium.
The present invention is to solve above-mentioned technical problem by following technical proposals:
The present invention provides a kind of control method of the automatic warm dish of smart machine, and the control method includes:
It obtains user in history set period of time and takes out tableware from smart machine in different usage time intervals daily
Historical time point；Wherein, the history set period of time includes N days, and N >=1 and N value are integer；
It is by n-th day in the history set period of time as input, usage time interval different in n-th day is corresponding
The historical time point establishes the time point for taking out tableware in different usage time intervals daily for obtaining user as output
Prediction model；Wherein, 1≤n≤N, n value are integer；
Obtain the M days in target time section；Wherein, 1≤M≤N, M value are integer；
It is input within M days the prediction model by the, obtaining the different of the M days in the target time section of user makes
The object time point of tableware is taken out with the period；
The warm dish function that duration opens the smart machine is set in advance according to the object time point.
Preferably, n-th day by the history set period of time is used as input, by use different in n-th day
The period corresponding historical time point is established and is taken out daily in different usage time intervals for obtaining user as output
The step of prediction model at the time point of tableware includes:
It is by n-th day in the history set period of time as input, usage time interval different in n-th day is corresponding
The historical time point carries out linear fit processing using least square method and obtains for obtaining user daily not as output
Same usage time interval takes out the prediction model at the time point of tableware.
Preferably, when the usage time interval includes breakfast time section, it is described will be in the history set period of time
As input, using the corresponding historical time point of usage time interval different in n-th day as output, foundation is used within n-th day
Obtain user includes: in the step of prediction model at the time point of different usage time interval taking-up tablewares daily
By n-th day in the history set period of time as input, described go through breakfast time section in n-th day is corresponding
History time point as output, establishes the first prediction mould for taking out the time point of tableware in breakfast time section daily for obtaining user
Type；
It is described to be input within M days the prediction model for the, obtain the M days difference of the user in the target time section
Usage time interval take out tableware object time point the step of include:
It is input within M days first prediction model by the, obtains the M days breakfast of user in the target time section
The object time point of period taking-up tableware；And/or
It is described that n-th in the history set period of time is heaven-made when the usage time interval includes the Chinese meal period
It is established using the corresponding historical time point of usage time interval different in n-th day as output for obtaining user for input
Include: in the step of prediction model at the time point of different usage time interval taking-up tablewares daily
By n-th day in the history set period of time as input, the Chinese meal period in n-th day corresponding described is gone through
History time point as output, establishes the second prediction mould for taking out the time point of tableware in the Chinese meal period daily for obtaining user
Type；
It is described to be input within M days the prediction model for the, obtain the M days difference of the user in the target time section
Usage time interval take out tableware object time point the step of include:
It is input within M days second prediction model by the, obtains the M days Chinese meal of the user in the target time section
The object time point of period taking-up tableware；And/or
It is described that n-th in the history set period of time is heaven-made when the usage time interval includes date for dinner section
It is established using the corresponding historical time point of usage time interval different in n-th day as output for obtaining user for input
Include: in the step of prediction model at the time point of different usage time interval taking-up tablewares daily
By n-th day in the history set period of time as input, described go through date for dinner section in n-th day is corresponding
History time point as output, establishes the third for obtaining the time point that user takes out tableware in date for dinner section daily and predicts mould
Type；
It is described to be input within M days the prediction model for the, obtain the M days difference of the user in the target time section
Usage time interval take out tableware object time point the step of include:
It is input within M days the third prediction model by the, obtains the M days dinner of the user in the target time section
The object time point of period taking-up tableware.
Preferably, the history set period of time is one month, the target time section is the history set period of time
Next month.
Preferably, the smart machine includes dish-washing machine or disinfection cabinet.
The present invention also provides a kind of control systems of the automatic warm dish of smart machine, and the control system includes historical time
Point obtains module, prediction model establishes module, time-obtaining module, object time point obtain module and warm dish control module；
The historical time point obtains module for obtaining in history set period of time user daily in different uses
Between section the historical time point of tableware is taken out from smart machine；Wherein, the history set period of time includes N days, N >=1 and N takes
Value is integer；
The prediction model establishes module for regarding n-th day in the history set period of time as input, by n-th day
As output, foundation makes for obtaining user daily in different the corresponding historical time point of middle different usage time interval
The prediction model at the time point of tableware is taken out with the period；Wherein, 1≤n≤N, n value are integer；
The time-obtaining module is used to obtain the M days in target time section；Wherein, 1≤M≤N, M value are integer；
The object time point obtains module and is used to be input within M days the prediction model for the, and user is in the mesh for acquisition
The different usage time intervals for marking the M days in the period take out the object time point of tableware；
The warm dish control module opens the smart machine for duration to be set in advance according to the object time point
Warm dish function.
It is inputted preferably, the prediction model establishes module for n-th day in the history set period of time to be used as,
Using the corresponding historical time point of usage time interval different in n-th day as output, carried out using least square method linear
Process of fitting treatment obtains the prediction model for taking out the time point of tableware in different usage time intervals daily for obtaining user.
Preferably, the prediction model is established module and is used for institute when the usage time interval includes breakfast time section
N-th day in history set period of time is stated as input, using the corresponding historical time point of breakfast time section in n-th day as
The first prediction model for taking out the time point of tableware in breakfast time section daily for obtaining user is established in output；
The object time point obtains module and is used to be input within M days first prediction model for the, and user is in institute for acquisition
State the object time point that the M days breakfast time sections in target time section take out tableware；And/or
When the usage time interval includes the Chinese meal period, the prediction model establishes module for setting the history
N-th day to fix time in section is built as input using the corresponding historical time point of Chinese meal period in n-th day as output
Found the second prediction model for taking out the time point of tableware in the Chinese meal period daily for obtaining user；
The object time point obtains module and is used to be input within M days second prediction model for the, and user is in institute for acquisition
State the object time point of the M days Chinese meal periods taking-up tableware in target time section；And/or
When the usage time interval includes date for dinner section, the prediction model establishes module for setting the history
N-th day to fix time in section is built as input using the corresponding historical time point of date for dinner section in n-th day as output
Found the third prediction model for taking out the time point of tableware in date for dinner section daily for obtaining user；
The object time point obtains module and is used to be input within M days the third prediction model for the, and user is in institute for acquisition
State the object time point that the M days date for dinner sections in target time section take out tableware.
Preferably, the history set period of time is one month, the target time section is the history set period of time
Next month.
Preferably, the smart machine includes dish-washing machine or disinfection cabinet.
The present invention also provides a kind of electronic equipment, including memory, processor and storage on a memory and can handled
The computer program run on device, the processor realize the automatic warm dish of above-mentioned smart machine when executing computer program
Control method.
The present invention also provides a kind of computer readable storage mediums, are stored thereon with computer program, the computer journey
The step of control method of automatic warm dish of the upper smart machine is realized when sequence is executed by processor.
The positive effect of the present invention is that:
In the present invention, (one month as above) user is daily in different meal time sections in the historical time section based on acquisition
The historical time point that tableware is taken out from dish-washing machine is established and takes out tableware in different usage time intervals daily for obtaining user
Time point prediction model, and then obtain user target time section (such as next month) daily section of different meal times from
The object time point of tableware is taken out in dish-washing machine, and opens the warm dish function of smart machine in advance before object time point, i.e.,
The use habit of user is obtained by analysis, adaptively tableware is heated in advance, to improve the use body of user
It tests.
Detailed description of the invention
Fig. 1 is the flow chart of the control method of the automatic warm dish of the smart machine of the embodiment of the present invention 1.
Fig. 2 is every in history set period of time in the control method of the automatic warm dish of the smart machine of the embodiment of the present invention 2
The pick-up of its Chinese meal period has time point distribution schematic diagram.
Fig. 3 is the flow chart of the control method of the automatic warm dish of the smart machine of the embodiment of the present invention 2.
Fig. 4 is the module diagram of the control system of the automatic warm dish of the smart machine of the embodiment of the present invention 3.
Fig. 5 is the structure of the electronic equipment of the control method of the automatic warm dish of the realization smart machine of the embodiment of the present invention 5
Schematic diagram.
Specific embodiment
The present invention is further illustrated below by the mode of embodiment, but does not therefore limit the present invention to the reality
It applies among a range.
Embodiment 1
As shown in Figure 1, the control method of the automatic warm dish of the smart machine of the present embodiment includes:
User takes out meal in different usage time intervals from smart machine daily in S101, acquisition history set period of time
The historical time point of tool；
Wherein, history set period of time includes N days, and N >=1 and N value are integer；
Tableware includes bowl, dish, spoon, fork, chopsticks etc..
S102, it regard n-th day in history set period of time as input, usage time interval different in n-th day is corresponding
Historical time point as output, establish the time point for daily taking out tableware in different usage time interval for obtaining user
Prediction model；
Wherein, 1≤n≤N, n value are integer；
The M days in S103, acquisition target time section；
Wherein, 1≤M≤N, M value are integer；
S104, the is input to prediction model for M days, obtains the M days different uses of the user in target time section
The object time point of period taking-up tableware；
S105, the warm dish function that duration opens smart machine is set in advance according to object time point.
Wherein, smart machine includes but is not limited to dish-washing machine, disinfection cabinet.
In the present embodiment, (one month as above) user is daily in the different meal times in the historical time section based on acquisition
Section takes out the historical time point of tableware from dish-washing machine, establishes and takes out meal in different usage time intervals daily for obtaining user
The prediction model at the time point of tool, and then obtain user's meal time section different daily in target time section (such as next month)
The object time point of tableware is taken out from dish-washing machine, and opens the warm dish function of smart machine in advance before object time point,
The use habit of user is obtained by analysis, adaptively tableware is heated in advance, to improve the use of user
Experience.
Embodiment 2
The control method of the automatic warm dish of the smart machine of the present embodiment is the further improvement to embodiment 1, specifically:
History set period of time is one month, and target time section is the next month of history set period of time；When some moon
31 days, then 1≤N≤31；When 30 days some moons, then 1≤N≤30.
Different usage time intervals includes breakfast time section, Chinese meal period and date for dinner section.
For example, breakfast time section includes 6:00-10:00, the Chinese meal period includes 10:00-14:00, date for dinner section packet
Include 16:00-20:00.
By some month user daily in case where Chinese meal period pick-up has, as shown in Fig. 2, laterally indicating this moon
Daily (unit are as follows: number of days), longitudinally indicate the historical time point that the daily Chinese meal user of this month has from smart machine pick-up,
The time habit having by the available user of the curve in Chinese meal period pick-up.
Specifically, when usage time interval includes breakfast time section, step S102 includes:
By n-th day in history set period of time as input, by the corresponding historical time point of breakfast time section in n-th day
As output, the first prediction model for taking out the time point of tableware in breakfast time section daily for obtaining user is established；
Step S104 includes:
It is input within M days the first prediction model by the, the M days breakfast time section of the user in target time section is obtained and takes
The object time point of tableware out.
When usage time interval includes the Chinese meal period, step S102 includes:
By n-th day in history set period of time as input, by the corresponding historical time point of Chinese meal period in n-th day
As output, the second prediction model for taking out the time point of tableware in the Chinese meal period daily for obtaining user is established；
Step S104 includes:
It is input within M days the second prediction model by the, user is obtained and takes the M days Chinese meal periods in target time section
The object time point of tableware out.
When usage time interval includes date for dinner section, step S102 includes:
By n-th day in history set period of time as input, by the corresponding historical time point of date for dinner section in n-th day
As output, the third prediction model for taking out the time point of tableware in date for dinner section daily for obtaining user is established；
Step S104 includes:
It is input within M days third prediction model by the, the M days date for dinner section of the user in target time section is obtained and takes
The object time point of tableware out.
As shown in figure 3, the step S102 of the present embodiment further include:
S1021, it regard n-th day in history set period of time as input, usage time interval different in n-th day is corresponding
Historical time point as output, using least square method carry out linear fit processing obtain for obtaining user daily in difference
Usage time interval take out tableware time point prediction model.
By some month user daily Chinese meal period pick-up have in case where, as shown in Fig. 2, according to the current time
The Chinese meal period in section predicts that the time point of the Chinese meal period in next period is unique certainly and tends to one
Time point, i.e., the use habit of user was fluctuated above and below some time point certainly, then the use habit of the user is (i.e.
The time point of pick-up tool) expression formula useable linear fitting expression Y=aX+b indicate, wherein when Y indicates Chinese meal some day
Between time point (such as 12:14 is simplified to 1214) for having of section user's pick-up, X indicates every day, and the value of two parameters of a, b passes through minimum
Square law, which carries out linear fit, can be obtained.
Specifically, linear fitting procedure is as follows:
1) one group of data (x is giveni,yi), i=0,1 ..., m-1.
Wherein, xiIndicate every day, yiIndicate the time point of Chinese meal some day period user pick-up tool.
2) fitting a straight line p (x)=a+bx, corresponding mean square error Q (a, b) are done are as follows:
Wherein, the minimum of Q (a, b) will meet following condition:
3) above-mentioned formula is organized into the form of following matrix:
4) equation is solved using the elimination or Gramer's method:
Two parameters of a, b in linear fit expression formula Y=aX+b can be solved respectively according to above-mentioned formula to get arriving
The prediction model for taking out the time point of tableware in different usage time intervals daily for obtaining user.
For example, it is 1135 that obtain a value, which be 2, b value, then obtain user daily the Chinese meal period take out tableware when
Between the second prediction model Y2 for putting are as follows: Y2=2*X2+1135.
If predicting, the 31st day user of next month takes out the time point of tableware: Y2=in the Chinese meal period from smart machine
2*31+1135=1197=12:37.
Similar above-mentioned linear fitting procedure is similarly available for obtaining user daily in the taking-up of breakfast time section
The first prediction model Y1 at the time point of tableware is also available for obtaining user daily in date for dinner section taking-up tableware
The third prediction model Y3 at time point, specific linear fitting procedure is similar to the above, therefore is not described in more detail here.
When smart machine includes dish-washing machine, before object time point Y1, Y2 and Y3 of the taking-up tableware of prediction reach,
The warm dish function of 10 minutes in advance unlatching dish-washing machines, this ensures that when user takes from dish-washing machine in object time point or so
The tableware that " warm " can be taken when tableware, that is, take the tableware of most thermophilic, to improve the usage experience of user.
In the present embodiment, (one month as above) user is daily in the different meal times in the historical time section based on acquisition
Section takes out the historical time point of tableware from dish-washing machine, establishes and takes out meal in different usage time intervals daily for obtaining user
The prediction model at the time point of tool, and then obtain user's meal time section different daily in target time section (such as next month)
The object time point of tableware is taken out from dish-washing machine, and opens the warm dish function of smart machine in advance before object time point,
The use habit of user is obtained by analysis, adaptively tableware is heated in advance, to improve the use of user
Experience.
Embodiment 3
As shown in figure 4, the control system of the automatic warm dish of the smart machine of the present embodiment includes that historical time point obtains mould
Block 1, prediction model establish module 2, time-obtaining module 3, object time point and obtain module 4 and warm dish control module 5.
Historical time point obtains module 1 for obtaining in history set period of time user daily in different usage time intervals
The historical time point of tableware is taken out from smart machine；Wherein, history set period of time includes N days, and N >=1 and N value are whole
Number；
Tableware includes bowl, dish, spoon, fork, chopsticks etc..
Prediction model is established module 2 and is inputted for n-th day in history set period of time to be used as, will be different in n-th day
The corresponding historical time point of usage time interval as output, foundation takes in different usage time intervals daily for obtaining user
The prediction model at the time point of tableware out；Wherein, 1≤n≤N, n value are integer；
Time-obtaining module 3 is used to obtain the M days in target time section；Wherein, 1≤M≤N, M value are integer；
Object time point obtains module 4 and is used to be input within M days prediction model for the, and user is in target time section for acquisition
The M days different usage time intervals take out the object time point of tableware；
Warm dish control module 5 is used to be set in advance the warm dish function that duration opens smart machine according to object time point.
Wherein, smart machine includes but is not limited to dish-washing machine, disinfection cabinet.
In the present embodiment, (one month as above) user is daily in the different meal times in the historical time section based on acquisition
Section takes out the historical time point of tableware from dish-washing machine, establishes and takes out meal in different usage time intervals daily for obtaining user
The prediction model at the time point of tool, and then obtain user's meal time section different daily in target time section (such as next month)
The object time point of tableware is taken out from dish-washing machine, and opens the warm dish function of smart machine in advance before object time point,
The use habit of user is obtained by analysis, adaptively tableware is heated in advance, to improve the use of user
Experience.
Embodiment 4
The control system of the automatic warm dish of the smart machine of the present embodiment is the further improvement to embodiment 3, specifically:
History set period of time is one month, and target time section is the next month of history set period of time；When some moon
31 days, then 1≤N≤31；When 30 days some moons, then 1≤N≤30.
Different usage time intervals includes breakfast time section, Chinese meal period and date for dinner section.
For example, breakfast time section includes 6:00-10:00, the Chinese meal period includes 10:00-14:00, date for dinner section packet
Include 16:00-20:00.
By some month user daily in case where Chinese meal period pick-up has, as shown in Fig. 2, laterally indicating this moon
Daily (unit are as follows: number of days), longitudinally indicate the historical time point that the daily Chinese meal user of this month has from smart machine pick-up,
The time habit having by the available user of the curve in Chinese meal period pick-up.Specifically, when usage time interval includes
When breakfast time section, prediction model is established module 2 and is inputted for n-th day in history set period of time to be used as, will be in n-th day
The corresponding historical time point of breakfast time section is established as output and takes out tableware in breakfast time section daily for obtaining user
First prediction model at time point；
Object time point obtains module 4 and is used to be input within M days the first prediction model for the, and user is in target time section for acquisition
In the M days breakfast time sections take out tableware object time point.
When usage time interval includes the Chinese meal period, prediction model establishes module 2 for will be in history set period of time
N-th day as input, using the corresponding historical time point of Chinese meal period in n-th day as export, foundation for obtaining user
Second prediction model at the time point of tableware is taken out in the Chinese meal period daily；
Object time point obtains module 4 and is used to be input within M days the second prediction model for the, and user is in target time section for acquisition
In the M days Chinese meal periods take out tableware object time point.
When usage time interval includes date for dinner section, prediction model establishes module 2 for will be in history set period of time
N-th day as input, using the corresponding historical time point of date for dinner section in n-th day as export, foundation for obtaining user
The third prediction model at the time point of tableware is taken out in date for dinner section daily；
Object time point obtains module 4 and is used to be input within M days third prediction model for the, and user is in target time section for acquisition
In the M days date for dinner sections take out tableware object time point.
Prediction model is established module 2 and is also used to n-th day in history set period of time as input, by n-th day not
The corresponding historical time point of same usage time interval carries out linear fit processing acquisition using least square method and is used for as output
Obtain the prediction model that user takes out the time point of tableware in different usage time intervals daily.
By some month user daily Chinese meal period pick-up have in case where, as shown in Fig. 2, according to the current time
The Chinese meal period in section predicts that the time point of the Chinese meal period in next period is unique certainly and tends to one
Time point, i.e., the use habit of user was fluctuated above and below some time point certainly, then the use habit of the user is (i.e.
The time point of pick-up tool) expression formula useable linear fitting expression Y=aX+b indicate, wherein when Y indicates Chinese meal some day
Between time point (such as 12:14 is simplified to 1214) for having of section user's pick-up, X indicates every day, and the value of two parameters of a, b passes through minimum
Square law, which carries out linear fit, can be obtained.
Specifically, linear fitting procedure is as follows:
1) one group of data (x is giveni,yi), i=0,1 ..., m-1.
Wherein, xiIndicate every day, yiIndicate the time point of Chinese meal some day period user pick-up tool.
2) fitting a straight line p (x)=a+bx, corresponding mean square error Q (a, b) are done are as follows:
Wherein, the minimum of Q (a, b) will meet following condition:
3) above-mentioned formula is organized into the form of following matrix:
4) equation is solved using the elimination or Gramer's method:
Two parameters of a, b in linear fit expression formula Y=aX+b can be solved respectively according to above-mentioned formula to get arriving
The prediction model for taking out the time point of tableware in different usage time intervals daily for obtaining user.
For example, it is 1135 that obtain a value, which be 2, b value, then obtain user daily the Chinese meal period take out tableware when
Between the second prediction model Y2 for putting are as follows: Y2=2*X2+1135.
If predicting, the 31st day user of next month takes out the time point of tableware: Y2=in the Chinese meal period from smart machine
2*31+1135=1197=12:37.
Similar above-mentioned linear fitting procedure is similarly available for obtaining user daily in the taking-up of breakfast time section
The first prediction model Y1 at the time point of tableware is also available for obtaining user daily in date for dinner section taking-up tableware
The third prediction model Y3 at time point, specific linear fitting procedure is similar to the above, therefore is not described in more detail here.
When smart machine includes dish-washing machine, before object time point Y1, Y2 and Y3 of the taking-up tableware of prediction reach,
The warm dish function of 10 minutes in advance unlatching dish-washing machines, this ensures that when user takes from dish-washing machine in object time point or so
The tableware that " warm " can be taken when tableware, that is, take the tableware of most thermophilic, to improve the usage experience of user.
In the present embodiment, (one month as above) user is daily in the different meal times in the historical time section based on acquisition
Section takes out the historical time point of tableware from dish-washing machine, establishes and takes out meal in different usage time intervals daily for obtaining user
The prediction model at the time point of tool, and then obtain user's meal time section different daily in target time section (such as next month)
The object time point of tableware is taken out from dish-washing machine, and opens the warm dish function of smart machine in advance before object time point,
The use habit of user is obtained by analysis, adaptively tableware is heated in advance, to improve the use of user
Experience.
Embodiment 5
Fig. 5 is the structural schematic diagram for a kind of electronic equipment that the embodiment of the present invention 5 provides.Electronic equipment include memory,
Processor and storage are on a memory and the computer program that can run on a processor, processor realize implementation when executing program
The control method of the automatic warm dish of smart machine in example 1 or 2 in any one embodiment.The electronic equipment 30 that Fig. 5 is shown is only
An example, should not function to the embodiment of the present invention and use scope bring any restrictions.
As shown in figure 5, electronic equipment 30 can be showed in the form of universal computing device, such as it can set for server
It is standby.The component of electronic equipment 30 can include but is not limited to: at least one above-mentioned processor 31, above-mentioned at least one processor
32, the bus 33 of different system components (including memory 32 and processor 31) is connected.
Bus 33 includes data/address bus, address bus and control bus.
Memory 32 may include volatile memory, such as random access memory (RAM) 321 and/or cache
Memory 322 can further include read-only memory (ROM) 323.
Memory 32 can also include program/utility 325 with one group of (at least one) program module 324, this
The program module 324 of sample includes but is not limited to: operating system, one or more application program, other program modules and journey
It may include the realization of network environment in ordinal number evidence, each of these examples or certain combination.
Processor 31 by operation storage computer program in memory 32, thereby executing various function application and
The control method of the automatic warm dish of smart machine in data processing, such as the embodiment of the present invention 1 or 2 in any one embodiment.
Electronic equipment 30 can also be communicated with one or more external equipments 34 (such as keyboard, sensing equipment etc.).It is this
Communication can be carried out by input/output (I/O) interface 35.Also, the equipment 30 that model generates can also pass through Network adaptation
Device 36 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network, such as internet) logical
Letter.As shown in figure 5, the other modules for the equipment 30 that network adapter 36 is generated by bus 33 and model communicate.It should be understood that
Although not shown in the drawings, the equipment 30 that can be generated with binding model uses other hardware and/or software module, including but unlimited
In: microcode, device driver, redundant processor, external disk drive array, RAID (disk array) system, magnetic tape drive
Device and data backup storage system etc..
It should be noted that although being referred to several units/modules or subelement/mould of electronic equipment in the above detailed description
Block, but it is this division be only exemplary it is not enforceable.In fact, embodiment according to the present invention, is retouched above
The feature and function for two or more units/modules stated can embody in a units/modules.Conversely, above description
A units/modules feature and function can with further division be embodied by multiple units/modules.
Embodiment 6
A kind of computer readable storage medium is present embodiments provided, computer program is stored thereon with, program is processed
The step in the control method of the automatic warm dish of the smart machine in embodiment 1 or 2 in any one embodiment is realized when device executes.
Wherein, what readable storage medium storing program for executing can use more specifically can include but is not limited to: portable disc, hard disk, random
Access memory, read-only memory, erasable programmable read only memory, light storage device, magnetic memory device or above-mentioned times
The suitable combination of meaning.
In possible embodiment, the present invention is also implemented as a kind of form of program product comprising program generation
Code, when program product is run on the terminal device, program code is appointed for executing terminal device in realization embodiment 1 or 2
Step in the control method of the automatic warm dish of smart machine in an embodiment of anticipating.
Wherein it is possible to be write with any combination of one or more programming languages for executing program of the invention
Code, program code can be executed fully on a user device, partly execute on a user device, is independent as one
Software package executes, part executes on a remote device or executes on a remote device completely on a user device for part.
Although specific embodiments of the present invention have been described above, it will be appreciated by those of skill in the art that this is only
For example, protection scope of the present invention is to be defined by the appended claims.Those skilled in the art without departing substantially from
Under the premise of the principle and substance of the present invention, many changes and modifications may be made, but these change and
Modification each falls within protection scope of the present invention.
Claims (12)
1. a kind of control method of the automatic warm dish of smart machine, which is characterized in that the control method includes:
Obtain the history that user in history set period of time takes out tableware in different usage time intervals from smart machine daily
Time point；Wherein, the history set period of time includes N days, and N >=1 and N value are integer；
It is by n-th day in the history set period of time as input, usage time interval different in n-th day is corresponding described
Historical time point as output, establish for obtain user taken out daily in different usage time interval tableware time point it is pre-
Survey model；Wherein, 1≤n≤N, n value are integer；
Obtain the M days in target time section；Wherein, 1≤M≤N, M value are integer；
It is input within M days the prediction model by the, when obtaining the M days different uses of the user in the target time section
Between section take out tableware object time point；
The warm dish function that duration opens the smart machine is set in advance according to the object time point.
2. the control method of the automatic warm dish of smart machine as described in claim 1, which is characterized in that described by the history
N-th day in set period of time as input, using the corresponding historical time point of usage time interval different in n-th day as
The step of exporting, establishing the prediction model for obtaining the time point that user takes out tableware in different usage time intervals daily packet
It includes:
It is by n-th day in the history set period of time as input, usage time interval different in n-th day is corresponding described
Historical time point carries out linear fit processing using least square method and obtains for obtaining user daily different as output
Usage time interval takes out the prediction model at the time point of tableware.
3. the control method of the automatic warm dish of smart machine as described in claim 1, which is characterized in that use the time when described
When section includes breakfast time section, n-th day by the history set period of time, will be different in n-th day as input
The corresponding historical time point of usage time interval is established as output for obtaining user daily in different usage time intervals
Take out tableware time point prediction model the step of include:
By n-th day in the history set period of time as input, when the history that breakfast time section in n-th day is corresponding
Between point as output, establish for obtain user daily breakfast time section take out tableware time point the first prediction model；
Described to be input within M days the prediction model for the, obtaining the different of the M days in the target time section of user makes
With the period take out tableware object time point the step of include:
It is input within M days first prediction model by the, obtains the M days breakfast time of the user in the target time section
The object time point of section taking-up tableware；And/or
When the usage time interval includes the Chinese meal period, n-th day by the history set period of time is as defeated
Enter, using the corresponding historical time point of usage time interval different in n-th day as output, establishes daily for obtaining user
Include: in the step of different usage time intervals takes out the prediction model at the time point of tableware
By n-th day in the history set period of time as input, when the history that the Chinese meal period in n-th day is corresponding
Between point as output, establish for obtain user daily the Chinese meal period take out tableware time point the second prediction model；
Described to be input within M days the prediction model for the, obtaining the different of the M days in the target time section of user makes
With the period take out tableware object time point the step of include:
It is input within M days second prediction model by the, obtains the M days Chinese meal time of the user in the target time section
The object time point of section taking-up tableware；And/or
When the usage time interval includes date for dinner section, n-th day by the history set period of time is as defeated
Enter, using the corresponding historical time point of usage time interval different in n-th day as output, establishes daily for obtaining user
Include: in the step of different usage time intervals takes out the prediction model at the time point of tableware
By n-th day in the history set period of time as input, when the history that date for dinner section in n-th day is corresponding
Between point as output, establish for obtain user daily date for dinner section take out tableware time point third prediction model；
Described to be input within M days the prediction model for the, obtaining the different of the M days in the target time section of user makes
With the period take out tableware object time point the step of include:
It is input within M days the third prediction model by the, obtains the M days date for dinner of the user in the target time section
The object time point of section taking-up tableware.
4. the control method of the automatic warm dish of smart machine as described in claim 1, which is characterized in that when the history is set
Between Duan Weiyi months, the target time section is the next month of the history set period of time.
5. the control method of the automatic warm dish of smart machine as described in claim 1, which is characterized in that the smart machine packet
Include dish-washing machine or disinfection cabinet.
6. a kind of control system of the automatic warm dish of smart machine, which is characterized in that the control system includes historical time point
Obtain module, prediction model establishes module, time-obtaining module, object time point obtain module and warm dish control module；
The historical time point obtains module for obtaining in history set period of time user daily in different usage time intervals
The historical time point of tableware is taken out from smart machine；Wherein, the history set period of time includes N days, N >=1 and N value is
Integer；
The prediction model establish module for by n-th day in the history set period of time as input, by n-th day not
With the corresponding historical time point of usage time interval as output, establish for obtaining user daily in different uses
Between section take out tableware time point prediction model；Wherein, 1≤n≤N, n value are integer；
The time-obtaining module is used to obtain the M days in target time section；Wherein, 1≤M≤N, M value are integer；
The object time point obtains module and is used to be input within M days the prediction model for the, and user is in the target for acquisition
Between the M days different usage time intervals in section take out the object time points of tablewares；
The warm dish control module is used to that the warm dish that duration opens the smart machine to be set in advance according to the object time point
Function.
7. the control system of the automatic warm dish of smart machine as claimed in claim 6, which is characterized in that the prediction model is built
Formwork erection block is used for n-th day in the history set period of time as input, and usage time interval different in n-th day is corresponding
The historical time point as output, carry out linear fit processing using least square method and obtain to exist daily for obtaining user
Different usage time intervals takes out the prediction model at the time point of tableware.
8. the control system of the automatic warm dish of smart machine as claimed in claim 6, which is characterized in that use the time when described
When section includes breakfast time section, the prediction model is established module and is used for the conduct in n-th day in the history set period of time
Input is established using the corresponding historical time point of breakfast time section in n-th day as output for obtaining user daily in morning
The meal period takes out first prediction model at the time point of tableware；
The object time point obtains module and is used to be input within M days first prediction model for the, and user is in the mesh for acquisition
Mark the object time point that the M days breakfast time sections in the period take out tableware；And/or
When the usage time interval includes the Chinese meal period, when the prediction model establishes module for setting the history
Between n-th day in section as input establish use using the corresponding historical time point of Chinese meal period in n-th day as exporting
Take out second prediction model at the time point of tableware in the Chinese meal period daily in acquisition user；
The object time point obtains module and is used to be input within M days second prediction model for the, and user is in the mesh for acquisition
Mark the object time point of the M days Chinese meal periods taking-up tableware in the period；And/or
When the usage time interval includes date for dinner section, when the prediction model establishes module for setting the history
Between n-th day in section as input establish use using the corresponding historical time point of date for dinner section in n-th day as exporting
Take out the third prediction model at the time point of tableware in date for dinner section daily in acquisition user；
The object time point obtains module and is used to be input within M days the third prediction model for the, and user is in the mesh for acquisition
Mark the object time point that the M days date for dinner sections in the period take out tableware.
9. the control system of the automatic warm dish of smart machine as claimed in claim 6, which is characterized in that when the history is set
Between Duan Weiyi months, the target time section is the next month of the history set period of time.
10. the control system of the automatic warm dish of smart machine as claimed in claim 6, which is characterized in that the smart machine
Including dish-washing machine or disinfection cabinet.
11. a kind of electronic equipment including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that the processor realizes intelligence of any of claims 1-5 when executing computer program
The control method of the automatic warm dish of equipment.
12. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of the control method of the automatic warm dish of smart machine of any of claims 1-5 is realized when being executed by processor
Suddenly.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910677687.5A CN110367897A (en) | 2019-07-25 | 2019-07-25 | Control method, system, equipment and the storage medium of the automatic warm dish of smart machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910677687.5A CN110367897A (en) | 2019-07-25 | 2019-07-25 | Control method, system, equipment and the storage medium of the automatic warm dish of smart machine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110367897A true CN110367897A (en) | 2019-10-25 |
Family
ID=68256069
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910677687.5A Pending CN110367897A (en) | 2019-07-25 | 2019-07-25 | Control method, system, equipment and the storage medium of the automatic warm dish of smart machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110367897A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111839396A (en) * | 2020-06-04 | 2020-10-30 | 佛山市百斯特电器科技有限公司 | Intelligent preheating method and device based on washing equipment |
| CN111839397A (en) * | 2020-06-04 | 2020-10-30 | 佛山市百斯特电器科技有限公司 | Intelligent preheating method and device based on washing equipment |
| CN112138194A (en) * | 2020-09-24 | 2020-12-29 | 广州市仪美医用家具科技股份有限公司 | Intelligent automatic sterilization wardrobe and sterilization control method and readable storage medium thereof |
| CN113487804A (en) * | 2021-06-23 | 2021-10-08 | 深圳市顾客王科技有限公司 | Temperature control method of meal taking cabinet, meal taking cabinet and computer readable storage medium |
| CN115365255A (en) * | 2022-01-07 | 2022-11-22 | 宁波方太厨具有限公司 | Garbage processor, starting method, system, equipment and storage medium thereof |
| WO2023143295A1 (en) * | 2022-01-26 | 2023-08-03 | 芜湖美的智能厨电制造有限公司 | Dishwasher control methods and apparatuses, dishwashers and computer storage medium |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2932197A1 (en) * | 2012-12-12 | 2015-10-21 | University Of North Dakota | Analyzing flight data using predictive models |
| CN106549833A (en) * | 2015-09-21 | 2017-03-29 | 阿里巴巴集团控股有限公司 | A kind of control method and device of intelligent home device |
| CN106652534A (en) * | 2016-12-14 | 2017-05-10 | 北京工业大学 | Method for predicting arrival time of bus |
| CN106952461A (en) * | 2017-02-27 | 2017-07-14 | 北京博纳电气股份有限公司 | A kind of Forecasting Methodology used for household electrical appliance |
| CN107917535A (en) * | 2016-10-10 | 2018-04-17 | 青岛经济技术开发区海尔热水器有限公司 | Method for predicting bathing habits and water heater |
| CN108062593A (en) * | 2017-10-31 | 2018-05-22 | 深圳和而泰智能控制股份有限公司 | Water use forecast method and prediction meanss |
| CN108805343A (en) * | 2018-05-29 | 2018-11-13 | 祝恩元 | A kind of Scientech Service Development horizontal forecast method based on multiple linear regression |
| CN108826701A (en) * | 2018-06-19 | 2018-11-16 | 青岛海尔科技有限公司 | A kind of intelligent control method and device of storage-type electric water heater |
| CN108919669A (en) * | 2018-09-11 | 2018-11-30 | 深圳和而泰数据资源与云技术有限公司 | A kind of smart home dynamic decision method, apparatus and service terminal |
| CN109086912A (en) * | 2018-07-11 | 2018-12-25 | 东北大学 | A kind of intelligent appliance user's behavior prediction method and apparatus based on LSTM |
| AU2018282300A1 (en) * | 2017-06-16 | 2019-01-17 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for allocating service requests |
| CN109282499A (en) * | 2017-07-21 | 2019-01-29 | 青岛经济技术开发区海尔热水器有限公司 | Method for predicting water consumption behavior of user for water heater and water heater |
| CN109287055A (en) * | 2018-11-14 | 2019-01-29 | 成都世纪光合作用科技有限公司 | A kind of method and device controlling Intelligent lamp |
| CN109670624A (en) * | 2018-05-08 | 2019-04-23 | 美味不用等（上海）信息科技股份有限公司 | A kind of method and device for estimating dining waiting time |
| CN109670277A (en) * | 2019-02-19 | 2019-04-23 | 南京邮电大学 | A kind of travel time prediction method based on multi-modal data fusion and multiple-model integration |
| JP6516282B2 (en) * | 2014-11-11 | 2019-05-22 | Ｎｅｃソリューションイノベータ株式会社 | Priority setting device, priority setting method, and program |
| CN109828479A (en) * | 2017-11-23 | 2019-05-31 | 宁波方太厨具有限公司 | A kind of disinfection cabinet warms up the intelligent control method of dish |
-
2019
- 2019-07-25 CN CN201910677687.5A patent/CN110367897A/en active Pending
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2932197A1 (en) * | 2012-12-12 | 2015-10-21 | University Of North Dakota | Analyzing flight data using predictive models |
| JP6516282B2 (en) * | 2014-11-11 | 2019-05-22 | Ｎｅｃソリューションイノベータ株式会社 | Priority setting device, priority setting method, and program |
| CN106549833A (en) * | 2015-09-21 | 2017-03-29 | 阿里巴巴集团控股有限公司 | A kind of control method and device of intelligent home device |
| CN107917535A (en) * | 2016-10-10 | 2018-04-17 | 青岛经济技术开发区海尔热水器有限公司 | Method for predicting bathing habits and water heater |
| CN106652534A (en) * | 2016-12-14 | 2017-05-10 | 北京工业大学 | Method for predicting arrival time of bus |
| CN106952461A (en) * | 2017-02-27 | 2017-07-14 | 北京博纳电气股份有限公司 | A kind of Forecasting Methodology used for household electrical appliance |
| AU2018282300A1 (en) * | 2017-06-16 | 2019-01-17 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for allocating service requests |
| CN109282499A (en) * | 2017-07-21 | 2019-01-29 | 青岛经济技术开发区海尔热水器有限公司 | Method for predicting water consumption behavior of user for water heater and water heater |
| CN108062593A (en) * | 2017-10-31 | 2018-05-22 | 深圳和而泰智能控制股份有限公司 | Water use forecast method and prediction meanss |
| CN109828479A (en) * | 2017-11-23 | 2019-05-31 | 宁波方太厨具有限公司 | A kind of disinfection cabinet warms up the intelligent control method of dish |
| CN109670624A (en) * | 2018-05-08 | 2019-04-23 | 美味不用等（上海）信息科技股份有限公司 | A kind of method and device for estimating dining waiting time |
| CN108805343A (en) * | 2018-05-29 | 2018-11-13 | 祝恩元 | A kind of Scientech Service Development horizontal forecast method based on multiple linear regression |
| CN108826701A (en) * | 2018-06-19 | 2018-11-16 | 青岛海尔科技有限公司 | A kind of intelligent control method and device of storage-type electric water heater |
| CN109086912A (en) * | 2018-07-11 | 2018-12-25 | 东北大学 | A kind of intelligent appliance user's behavior prediction method and apparatus based on LSTM |
| CN108919669A (en) * | 2018-09-11 | 2018-11-30 | 深圳和而泰数据资源与云技术有限公司 | A kind of smart home dynamic decision method, apparatus and service terminal |
| CN109287055A (en) * | 2018-11-14 | 2019-01-29 | 成都世纪光合作用科技有限公司 | A kind of method and device controlling Intelligent lamp |
| CN109670277A (en) * | 2019-02-19 | 2019-04-23 | 南京邮电大学 | A kind of travel time prediction method based on multi-modal data fusion and multiple-model integration |
Non-Patent Citations (1)
| Title |
|---|
| 王恺颖: "智能家居关键控制技术及系统研究", 《工程科技Ⅱ辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111839396A (en) * | 2020-06-04 | 2020-10-30 | 佛山市百斯特电器科技有限公司 | Intelligent preheating method and device based on washing equipment |
| CN111839397A (en) * | 2020-06-04 | 2020-10-30 | 佛山市百斯特电器科技有限公司 | Intelligent preheating method and device based on washing equipment |
| CN111839396B (en) * | 2020-06-04 | 2021-11-02 | 佛山市百斯特电器科技有限公司 | Intelligent preheating method and device based on washing equipment |
| CN111839397B (en) * | 2020-06-04 | 2022-02-01 | 佛山市百斯特电器科技有限公司 | Intelligent preheating method and device based on washing equipment |
| CN112138194A (en) * | 2020-09-24 | 2020-12-29 | 广州市仪美医用家具科技股份有限公司 | Intelligent automatic sterilization wardrobe and sterilization control method and readable storage medium thereof |
| CN112138194B (en) * | 2020-09-24 | 2022-05-31 | 广州市仪美医用家具科技股份有限公司 | Intelligent automatic sterilization wardrobe and sterilization control method and readable storage medium thereof |
| CN113487804A (en) * | 2021-06-23 | 2021-10-08 | 深圳市顾客王科技有限公司 | Temperature control method of meal taking cabinet, meal taking cabinet and computer readable storage medium |
| CN113487804B (en) * | 2021-06-23 | 2022-07-26 | 深圳市顾客王科技有限公司 | Temperature control method of meal taking cabinet, meal taking cabinet and computer readable storage medium |
| CN115365255A (en) * | 2022-01-07 | 2022-11-22 | 宁波方太厨具有限公司 | Garbage processor, starting method, system, equipment and storage medium thereof |
| WO2023143295A1 (en) * | 2022-01-26 | 2023-08-03 | 芜湖美的智能厨电制造有限公司 | Dishwasher control methods and apparatuses, dishwashers and computer storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110367897A (en) | Control method, system, equipment and the storage medium of the automatic warm dish of smart machine | |
| US9104572B1 (en) | Automated root cause analysis | |
| CN109558234A (en) | A kind of timed task dispatching method and device | |
| CN109783023B (en) | Method and related device for data scrubbing | |
| CN108804231A (en) | A kind of Memory Optimize Method, device, readable storage medium storing program for executing and mobile terminal | |
| CN107944000A (en) | Flight freight rate update method, device, electronic equipment, storage medium | |
| CN115543577B (en) | Covariate-based Kubernetes resource scheduling optimization method, storage medium and device | |
| CN103810099B (en) | Code tracing method and code tracing system | |
| CN106798964A (en) | Family expenses electrotherapy instrument system and electro-therapeutic apparatus control method | |
| CN109117141A (en) | Simplify method, apparatus, the electronic equipment, computer readable storage medium of programming | |
| CN109634714B (en) | Intelligent scheduling method and device | |
| CN107819815A (en) | Electric meter fault reminding method and Internet of things system based on compound Internet of Things | |
| CN103578020A (en) | Commodity information pushing system and method | |
| US12061895B2 (en) | Predictive recycling of computer systems in a cloud environment | |
| CN105450737A (en) | Data processing method, data processing device and data processing system | |
| CN102681650B (en) | The storage system of a kind of Energy control power-economizing method and correspondence thereof | |
| CN110188297B (en) | Resource information display method, computing device and computer storage medium | |
| CN105321124A (en) | Hadoop-based electric power cloud platform design scheme | |
| CN108196844A (en) | Information processing method, device, electronic equipment and storage medium | |
| CN104536736A (en) | Server and method thereof, and systems and method thereof | |
| EP2005268A1 (en) | System and method to allow non-deterministic execution in a process control system | |
| Bacou et al. | Drowsy-DC: Data center power management system | |
| CN105183432A (en) | Health management oriented SOC system | |
| CN110231969A (en) | User interface management method, apparatus, computer installation and readable storage medium storing program for executing | |
| CN109783217A (en) | Shut down determination method, apparatus, electronic equipment and the storage medium of period |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191025 |